support CUDA Graph for new executor (#49708)

* new exe supports CUDA Graph

* fix

* fix

* fix

* fix FLAGS_use_stream_safe_cuda_allocator in unittest

* insert output of coalesce_tensor op to skip_gc_var

* fix

paddle/fluid/framework/new_executor/garbage_collector/garbage_collector.cc

@@ -19,14 +19,27 @@
 #include "paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.h"
 
 DECLARE_bool(fast_eager_deletion_mode);
+DECLARE_bool(new_executor_use_cuda_graph);
 
 namespace paddle {
 namespace framework {
 
 bool IsInterpretercoreFastGCEnabled() {
-  return memory::allocation::AllocatorFacade::Instance()
-             .IsStreamSafeCUDAAllocatorUsed() &&
-         FLAGS_fast_eager_deletion_mode;
+  // When using cuda graph, fast GC must be used. Because
+  // `EventQuery` method in event GC cannot be used in
+  // cuda graph.
+  PADDLE_ENFORCE_EQ(memory::allocation::AllocatorFacade::Instance()
+                                .IsStreamSafeCUDAAllocatorUsed() == false &&
+                        FLAGS_new_executor_use_cuda_graph,
+                    false,
+                    platform::errors::InvalidArgument(
+                        "When FLAGS_new_executor_use_cuda_graph is true, "
+                        "IsStreamSafeCUDAAllocatorUsed must be true, but "
+                        "got false."));
+  return (memory::allocation::AllocatorFacade::Instance()
+              .IsStreamSafeCUDAAllocatorUsed() &&
+          FLAGS_fast_eager_deletion_mode) ||
+         FLAGS_new_executor_use_cuda_graph;
 }
 
 InterpreterCoreGarbageCollector::InterpreterCoreGarbageCollector() {

paddle/fluid/framework/new_executor/interpretercore.cc

@@ -31,6 +31,7 @@
 #ifdef PADDLE_WITH_MKLDNN
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #endif
+#include "paddle/fluid/platform/cuda_graph_with_memory_pool.h"
 #include "paddle/phi/backends/device_manager.h"
 
 PADDLE_DEFINE_EXPORTED_bool(
@@ -50,6 +51,10 @@ PADDLE_DEFINE_EXPORTED_bool(control_flow_use_new_executor,
 
 DECLARE_bool(check_nan_inf);
 DECLARE_bool(benchmark);
+DECLARE_bool(new_executor_use_cuda_graph);
+#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
+DECLARE_bool(sync_nccl_allreduce);
+#endif
 
 constexpr const char* kExceptionCaught = "ExceptionCaught";
 constexpr const char* kTaskCompletion = "TaskCompletion";
@@ -142,6 +147,8 @@ InterpreterCore::InterpreterCore(const platform::Place& place,
     }
     return lhs_prority > rhs_prority;
   };
+
+  PrepareForCUDAGraphCapture();
 }
 
 InterpreterCore::~InterpreterCore() {
@@ -161,6 +168,7 @@ interpreter::CostInfo InterpreterCore::DryRun(
     const std::vector<std::string>& feed_names,
     const std::vector<phi::DenseTensor>& feed_tensors) {
   SetDeviceId(place_);
+  CheckCUDAGraphBeforeRun(feed_names);
 
   Prepare(feed_names, feed_tensors, true);
   interpreter::CostInfo cost_info;
@@ -221,6 +229,7 @@ paddle::framework::FetchList InterpreterCore::Run(
     const std::vector<std::string>& feed_names,
     const std::vector<phi::DenseTensor>& feed_tensors) {
   SetDeviceId(place_);
+  CheckCUDAGraphBeforeRun(feed_names);
 
 #ifdef PADDLE_WITH_MKLDNN
   platform::AttachPointerHashToMKLDNNKey(this, place_);
@@ -240,7 +249,16 @@ paddle::framework::FetchList InterpreterCore::Run(
   // return Fetch Tensors
   auto* fetch_var = local_scope_->FindVar(interpreter::kFetchVarName);
   if (fetch_var) {
-    return std::move(*fetch_var->GetMutable<framework::FetchList>());
+    auto fetch_list = std::move(*fetch_var->GetMutable<framework::FetchList>());
+#ifdef PADDLE_WITH_CUDA
+    if (platform::IsCUDAGraphCapturing()) {
+      PADDLE_ENFORCE_EQ(fetch_list.empty(),
+                        true,
+                        platform::errors::InvalidArgument(
+                            "Cannot fetch data when using CUDA Graph."));
+    }
+#endif
+    return fetch_list;
   } else {
     return {};
   }
@@ -249,6 +267,7 @@ paddle::framework::FetchList InterpreterCore::Run(
 paddle::framework::FetchList InterpreterCore::Run(
     const std::vector<std::string>& feed_names, bool need_fetch) {
   SetDeviceId(place_);
+  CheckCUDAGraphBeforeRun(feed_names);
 
 #ifdef PADDLE_WITH_MKLDNN
   platform::AttachPointerHashToMKLDNNKey(this, place_);
@@ -290,7 +309,16 @@ paddle::framework::FetchList InterpreterCore::Run(
       HasLocalScope() ? local_scope_ : var_scope_.GetMutableScope();
   auto* fetch_var = inner_scope->FindVar(interpreter::kFetchVarName);
   if (fetch_var && need_fetch) {
-    return std::move(*fetch_var->GetMutable<framework::FetchList>());
+    auto fetch_list = std::move(*fetch_var->GetMutable<framework::FetchList>());
+#ifdef PADDLE_WITH_CUDA
+    if (platform::IsCUDAGraphCapturing()) {
+      PADDLE_ENFORCE_EQ(fetch_list.empty(),
+                        true,
+                        platform::errors::InvalidArgument(
+                            "Cannot fetch data when using CUDA Graph."));
+    }
+#endif
+    return fetch_list;
   } else {
     return {};
   }
@@ -504,6 +532,67 @@ void InterpreterCore::BuildInplace() {
   }
 }
 
+void InterpreterCore::PrepareForCUDAGraphCapture() {
+  if (!FLAGS_new_executor_use_cuda_graph) return;
+#ifdef PADDLE_WITH_CUDA
+  PADDLE_ENFORCE_EQ(
+      platform::IsCUDAGraphCapturing(),
+      false,
+      platform::errors::PermissionDenied("CUDA Graph is not allowed to capture "
+                                         "when running the first batch."));
+  PADDLE_ENFORCE_EQ(platform::is_gpu_place(place_),
+                    true,
+                    platform::errors::InvalidArgument(
+                        "CUDA Graph is only supported on NVIDIA GPU device."));
+  // If set true, will call `cudaStreamSynchronize(nccl_stream)`after allreduce.
+  // which may cause error in cuda graph. This behavior is consistent with PE.
+  PADDLE_ENFORCE_EQ(FLAGS_sync_nccl_allreduce,
+                    false,
+                    platform::errors::InvalidArgument(
+                        "FLAGS_sync_nccl_allreduce must be False to support "
+                        "CUDA Graph capturing."));
+
+  // All output vars of coalesce_tensor op should not be gc.
+  // If fused output var of coalesce_tensor is gc, it will cause accuracy
+  // problem. The specific reasons need to be analyzed.
+  for (auto& op_desc : block_.AllOps()) {
+    if (op_desc->Type() == kCoalesceTensor) {
+      for (auto& out_var_name : op_desc->OutputArgumentNames()) {
+        execution_config_.skip_gc_vars.insert(out_var_name);
+        VLOG(4) << "Insert Var(" << out_var_name << ") into skip_gc_vars.";
+      }
+    }
+  }
+#else
+  PADDLE_THROW(platform::errors::Unimplemented(
+      "CUDA Graph is only supported on NVIDIA GPU device."));
+#endif
+}
+
+void InterpreterCore::CheckCUDAGraphBeforeRun(
+    const std::vector<std::string>& feed_names) {
+#ifdef PADDLE_WITH_CUDA
+  if (platform::IsCUDAGraphCapturing()) {
+    PADDLE_ENFORCE_EQ(
+        feed_names.empty(),
+        true,
+        platform::errors::InvalidArgument(
+            "Feeding data is not permitted when capturing CUDA Graph."));
+    PADDLE_ENFORCE_EQ(
+        FLAGS_new_executor_use_cuda_graph,
+        true,
+        platform::errors::InvalidArgument(
+            "You must turn on FLAGS_new_executor_use_cuda_graph to True "
+            "to enable CUDA Graph capturing."));
+    PADDLE_ENFORCE_EQ(
+        place_,
+        platform::CUDAGraphCapturingPlace(),
+        platform::errors::InvalidArgument("The place to capture CUDAGraph is "
+                                          "not the same as the place to run."));
+  }
+#endif
+}
+
 void InterpreterCore::BuildOperatorDependences() {
   // analysis the dependences between ops, add next_instr_list to each instr,
   // and set the dependecy_count_

paddle/fluid/framework/new_executor/interpretercore.h

@@ -97,6 +97,10 @@ class InterpreterCore {
       const std::vector<std::vector<size_t>>& input_var2op, size_t var_index);
   void SetFeedVarsInplaceSkip(const std::vector<std::string>& feed_names);
 
+  // cuda graph
+  void CheckCUDAGraphBeforeRun(const std::vector<std::string>& feed_names);
+  void PrepareForCUDAGraphCapture();
+
   // execution
   void RunImpl();
   void ExecuteInstructionList(const std::vector<Instruction>& vec_instr);

paddle/fluid/platform/cuda_graph_with_memory_pool.cc

@@ -18,6 +18,7 @@
 #include "paddle/phi/backends/all_context.h"
 
 DECLARE_bool(use_stream_safe_cuda_allocator);
+DECLARE_bool(new_executor_use_cuda_graph);
 
 namespace paddle {
 namespace platform {
@@ -43,7 +44,10 @@ void BeginCUDAGraphCapture(phi::GPUPlace place,
   auto stream = dev_ctx->stream();
   CUDAGraph::BeginCapture(place, stream, mode);
 
-  auto old_value = FLAGS_use_stream_safe_cuda_allocator;
+  // When using cuda graph in new executor, fast GC must be used.
+  // FLAGS_use_stream_safe_cuda_allocator should be true.
+  auto old_value = FLAGS_use_stream_safe_cuda_allocator &&
+                   !FLAGS_new_executor_use_cuda_graph;
   if (old_value) {
     FLAGS_use_stream_safe_cuda_allocator = false;
   }

paddle/phi/core/flags.cc

@@ -1010,6 +1010,18 @@ PADDLE_DEFINE_EXPORTED_bool(enable_cinn_auto_tune,
 
 #endif
 
+/*
+ * CUDA Graph related FLAG
+ * Name: FLAGS_new_executor_use_cuda_graph
+ * Since Version: 2.4
+ * Value Range: bool, default=false
+ * Example: FLAGS_new_executor_use_cuda_graph=true would allow
+ * new executor to use CUDA Graph.
+ */
+PADDLE_DEFINE_EXPORTED_bool(new_executor_use_cuda_graph,
+                            false,
+                            "Use CUDA Graph in new executor");
+
 DEFINE_int32(record_pool_max_size,
              2000000,
              "SlotRecordDataset slot record pool max size");

python/paddle/fluid/executor.py

@@ -26,6 +26,7 @@ from .framework import convert_np_dtype_to_dtype_, _apply_pass
 from . import core
 from . import unique_name
 from . import compiler
+from . import set_flags
 from .trainer_factory import TrainerFactory
 from .trainer_factory import FetchHandlerMonitor
 import copy
@@ -510,6 +511,16 @@ def _is_dy2st_enable_standalone_executor():
     ]
 
 
+def _is_cuda_graph_enable_standalone_executor():
+    return framework._cuda_graph_enable_standalone_executor_ in [
+        1,
+        '1',
+        True,
+        'True',
+        'true',
+    ]
+
+
 def _prepare_fleet_executor():
     from ..distributed.fleet.proto import fleet_executor_desc_pb2
 
@@ -844,7 +855,19 @@ class _ExecutorCache:
             )
             build_strategy = compiled_program._build_strategy
             # print(f"Program before convert:\n {inner_program}", flush=True)
+            use_cuda_graph = False
+            # When using cuda graph, the cuda graph preparation logic in PE is not
+            # executed, but it is processed in the constructor of new executor.
+            if (
+                build_strategy is not None
+                and build_strategy.allow_cuda_graph_capture
+            ):
+                use_cuda_graph = True
+                build_strategy.allow_cuda_graph_capture = False
+                set_flags({"FLAGS_new_executor_use_cuda_graph": True})
             compiled_program._compile(scope, place)
+            if use_cuda_graph:
+                build_strategy.allow_cuda_graph_capture = True
             ir_graph = framework.IrGraph(compiled_program._graph)
             converted_program = ir_graph.to_program()
 
@@ -1746,24 +1769,25 @@ class Executor:
                     )
                     return False
 
-                # Unsupported case 4: CUDA Graph
+                # Unsupported case 4: async mode
                 if (
                     compiled_program._build_strategy is not None
-                    and compiled_program._build_strategy.allow_cuda_graph_capture
+                    and compiled_program._build_strategy.async_mode
                 ):
                     warnings.warn(
-                        "Standalone executor is not used for CUDA Graph",
+                        "Standalone executor is not used for async mode",
                         UserWarning,
                     )
                     return False
 
-                # Unsupported case 5: async mode
+                # Unsupported case 5: CUDA Graph
                 if (
                     compiled_program._build_strategy is not None
-                    and compiled_program._build_strategy.async_mode
+                    and compiled_program._build_strategy.allow_cuda_graph_capture
+                    and not _is_cuda_graph_enable_standalone_executor()
                 ):
                     warnings.warn(
-                        "Standalone executor is not used for async mode",
+                        "Standalone executor is not used for CUDA Graph when FLAGS_CUDA_GRAPH_USE_STANDALONE_EXECUTOR=0",
                         UserWarning,
                     )
                     return False
@@ -1811,8 +1835,13 @@ class Executor:
                 tensor = core.get_variable_tensor(scope, lr_sheduler._var_name)
                 # NOTE(dev): `tensor.set(data, self.place)` always call TensorCopySync that is a blocking behavior. So we use `_copy_from` to replace it.
                 cpu_tensor = _as_lodtensor(data, core.CPUPlace())
-                # for ipu, tensor is allocated on cpu
-                if core.is_compiled_with_ipu():
+                if core.is_cuda_graph_capturing():
+                    warnings.warn(
+                        "Caution!!! When capturing CUDA Graph, the learning rate scheduler would not "
+                        "take any effect! Please set the learning rate manually before each batch!"
+                    )
+                elif core.is_compiled_with_ipu():
+                    # for ipu, tensor is allocated on cpu
                     tensor._copy_from(cpu_tensor, tensor._place())
                 else:
                     tensor._copy_from(cpu_tensor, self.place)

python/paddle/fluid/framework.py

@@ -86,6 +86,9 @@ _enable_standalone_executor_ = os.environ.get(
 _dy2st_enable_standalone_executor_ = os.environ.get(
     'FLAGS_DY2ST_USE_STANDALONE_EXECUTOR', 1
 )
+_cuda_graph_enable_standalone_executor_ = os.environ.get(
+    'FLAGS_CUDA_GRAPH_USE_STANDALONE_EXECUTOR', 0
+)
 
 # Some explanation of our execution system 2022.03
 # For now we have 3 kinds of execution system, since we refactored dygraph mode to

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -1259,3 +1259,7 @@ set_tests_properties(test_parallel_executor_dry_run
                      PROPERTIES ENVIRONMENT "FLAGS_USE_STANDALONE_EXECUTOR=0")
 set_tests_properties(test_parallel_executor_drop_scope
                      PROPERTIES ENVIRONMENT "FLAGS_USE_STANDALONE_EXECUTOR=0")
+
+set_tests_properties(
+  test_cuda_graph_static_mode
+  PROPERTIES ENVIRONMENT "FLAGS_CUDA_GRAPH_USE_STANDALONE_EXECUTOR=1")

python/paddle/fluid/tests/unittests/test_cuda_graph.py

@@ -18,18 +18,16 @@ import shutil
 import unittest
 
 import numpy as np
-from simple_nets import simple_fc_net_with_inputs
 
 import paddle
 from paddle.device.cuda.graphs import CUDAGraph
-from paddle.fluid.dygraph.base import switch_to_static_graph
 
 
 def can_use_cuda_graph():
     return paddle.is_compiled_with_cuda() and not paddle.is_compiled_with_rocm()
 
 
-class TestCUDAGraph(unittest.TestCase):
+class TestCUDAGraphInDygraphMode(unittest.TestCase):
     def setUp(self):
         if can_use_cuda_graph():
             paddle.set_flags(
@@ -46,94 +44,6 @@ class TestCUDAGraph(unittest.TestCase):
             np.random.randint(low=0, high=10, size=shape).astype("float32")
         )
 
-    @switch_to_static_graph
-    def test_cuda_graph_static_graph(self):
-        if not can_use_cuda_graph():
-            return
-
-        seed = 100
-        loss_cuda_graph = self.cuda_graph_static_graph_main(
-            seed, use_cuda_graph=True
-        )
-        loss_no_cuda_graph = self.cuda_graph_static_graph_main(
-            seed, use_cuda_graph=False
-        )
-        self.assertEqual(loss_cuda_graph, loss_no_cuda_graph)
-
-    def cuda_graph_static_graph_main(self, seed, use_cuda_graph):
-        batch_size = 1
-        class_num = 10
-        image_shape = [batch_size, 784]
-        label_shape = [batch_size, 1]
-
-        paddle.seed(seed)
-        np.random.seed(seed)
-        startup = paddle.static.Program()
-        main = paddle.static.Program()
-        with paddle.static.program_guard(main, startup):
-            image = paddle.static.data(
-                name="image", shape=image_shape, dtype='float32'
-            )
-            label = paddle.static.data(
-                name="label", shape=label_shape, dtype='int64'
-            )
-            image.persistable = True
-            label.persistable = True
-            loss = simple_fc_net_with_inputs(image, label, class_num)
-            loss.persistable = True
-            lr = paddle.optimizer.lr.PiecewiseDecay(
-                boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04]
-            )
-            optimizer = paddle.optimizer.SGD(learning_rate=lr)
-            optimizer.minimize(loss)
-        place = paddle.CUDAPlace(0)
-        exe = paddle.static.Executor(place)
-        scope = paddle.static.Scope()
-        with paddle.static.scope_guard(scope):
-            exe.run(startup)
-            build_strategy = paddle.static.BuildStrategy()
-            build_strategy.allow_cuda_graph_capture = True
-            build_strategy.fix_op_run_order = True
-            build_strategy.fuse_all_optimizer_ops = True
-            compiled_program = paddle.static.CompiledProgram(
-                main
-            ).with_data_parallel(
-                loss_name=loss.name, build_strategy=build_strategy, places=place
-            )
-            image_t = scope.var(image.name).get_tensor()
-            label_t = scope.var(label.name).get_tensor()
-            loss_t = scope.var(loss.name).get_tensor()
-            lr_var = main.global_block().var(lr._var_name)
-            self.assertTrue(lr_var.persistable)
-            lr_t = scope.var(lr_var.name).get_tensor()
-            cuda_graph = None
-            for batch_id in range(20):
-                image_t.set(
-                    np.random.rand(*image_shape).astype('float32'), place
-                )
-                label_t.set(
-                    np.random.randint(
-                        low=0, high=class_num, size=label_shape, dtype='int64'
-                    ),
-                    place,
-                )
-
-                if batch_id == 1 and use_cuda_graph:
-                    cuda_graph = CUDAGraph(place, mode="global")
-                    cuda_graph.capture_begin()
-                    exe.run(compiled_program)
-                    cuda_graph.capture_end()
-
-                if cuda_graph:
-                    lr_t.set(np.array([lr()], dtype='float32'), place)
-                    cuda_graph.replay()
-                else:
-                    exe.run(compiled_program)
-                lr.step()
-            if cuda_graph:
-                cuda_graph.reset()
-        return np.array(loss_t)
-
     def test_cuda_graph_dynamic_graph(self):
         if not can_use_cuda_graph():
             return

python/paddle/fluid/tests/unittests/test_cuda_graph_static_mode.py

+# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+from simple_nets import simple_fc_net_with_inputs
+
+import paddle
+from paddle.device.cuda.graphs import CUDAGraph
+from paddle.fluid.dygraph.base import switch_to_static_graph
+
+
+def can_use_cuda_graph():
+    return paddle.is_compiled_with_cuda() and not paddle.is_compiled_with_rocm()
+
+
+class TestCUDAGraphInStaticMode(unittest.TestCase):
+    def setUp(self):
+        if can_use_cuda_graph():
+            # The behavior of `FLAGS_use_stream_safe_cuda_allocator` in static
+            # mode is inconsistent with that in dygraph mode.
+            # In static mode, FLAGS_use_stream_safe_cuda_allocator must be True.
+            # In dygraph mode, FLAGS_use_stream_safe_cuda_allocator must be False.
+            # These two types of unittests need to be written separately, because
+            # the allocator may only be initialized once, and the flag
+            # `FLAGS_use_stream_safe_cuda_allocator` only takes effect during
+            # initialization.
+            paddle.set_flags(
+                {
+                    'FLAGS_allocator_strategy': 'auto_growth',
+                    'FLAGS_sync_nccl_allreduce': False,
+                    'FLAGS_cudnn_deterministic': True,
+                    'FLAGS_use_stream_safe_cuda_allocator': True,
+                }
+            )
+
+    @switch_to_static_graph
+    def test_cuda_graph_static_graph(self):
+        if not can_use_cuda_graph():
+            return
+
+        seed = 100
+        loss_cuda_graph = self.cuda_graph_static_graph_main(
+            seed, use_cuda_graph=True
+        )
+        loss_no_cuda_graph = self.cuda_graph_static_graph_main(
+            seed, use_cuda_graph=False
+        )
+        self.assertEqual(loss_cuda_graph, loss_no_cuda_graph)
+
+    def cuda_graph_static_graph_main(self, seed, use_cuda_graph):
+        batch_size = 1
+        class_num = 10
+        image_shape = [batch_size, 784]
+        label_shape = [batch_size, 1]
+
+        paddle.seed(seed)
+        np.random.seed(seed)
+        startup = paddle.static.Program()
+        main = paddle.static.Program()
+        with paddle.static.program_guard(main, startup):
+            image = paddle.static.data(
+                name="image", shape=image_shape, dtype='float32'
+            )
+            label = paddle.static.data(
+                name="label", shape=label_shape, dtype='int64'
+            )
+            image.persistable = True
+            label.persistable = True
+            loss = simple_fc_net_with_inputs(image, label, class_num)
+            loss.persistable = True
+            lr = paddle.optimizer.lr.PiecewiseDecay(
+                boundaries=[2, 3, 4], values=[0.01, 0.02, 0.03, 0.04]
+            )
+            optimizer = paddle.optimizer.SGD(learning_rate=lr)
+            optimizer.minimize(loss)
+        place = paddle.CUDAPlace(0)
+        exe = paddle.static.Executor(place)
+        scope = paddle.static.Scope()
+        with paddle.static.scope_guard(scope):
+            exe.run(startup)
+            build_strategy = paddle.static.BuildStrategy()
+            build_strategy.allow_cuda_graph_capture = True
+            build_strategy.fix_op_run_order = True
+            build_strategy.fuse_all_optimizer_ops = True
+            compiled_program = paddle.static.CompiledProgram(
+                main
+            ).with_data_parallel(
+                loss_name=loss.name, build_strategy=build_strategy, places=place
+            )
+            image_t = scope.var(image.name).get_tensor()
+            label_t = scope.var(label.name).get_tensor()
+            loss_t = scope.var(loss.name).get_tensor()
+            lr_var = main.global_block().var(lr._var_name)
+            self.assertTrue(lr_var.persistable)
+            lr_t = scope.var(lr_var.name).get_tensor()
+            cuda_graph = None
+            for batch_id in range(20):
+                image_t.set(
+                    np.random.rand(*image_shape).astype('float32'), place
+                )
+                label_t.set(
+                    np.random.randint(
+                        low=0, high=class_num, size=label_shape, dtype='int64'
+                    ),
+                    place,
+                )
+
+                if batch_id == 1 and use_cuda_graph:
+                    cuda_graph = CUDAGraph(place, mode="global")
+                    cuda_graph.capture_begin()
+                    exe.run(compiled_program)
+                    cuda_graph.capture_end()
+
+                if cuda_graph:
+                    lr_t.set(np.array([lr()], dtype='float32'), place)
+                    cuda_graph.replay()
+                else:
+                    exe.run(compiled_program)
+                lr.step()
+            if cuda_graph:
+                cuda_graph.reset()
+        return np.array(loss_t)
+
+
+if __name__ == "__main__":
+    unittest.main()

tools/parallel_UT_rule.py

@@ -623,6 +623,7 @@ HIGH_PARALLEL_JOB_NEW = [
     'test_dataset_consistency_inspection',
     'test_cuda_empty_cache',
     'test_cuda_graph',
+    'test_cuda_graph_static_mode',
     'test_disable_signal_handler',
     'test_eig_op',
     'test_eigh_op',
@@ -2509,6 +2510,7 @@ TETRAD_PARALLEL_JOB = [
     'test_dlpack',
     'test_complex_variable',
     'test_cuda_graph',
+    'test_cuda_graph_static_mode',
     'test_custom_grad_input',
     'test_accuracy_op',
     'test_pool1d_api',